Proteomic profiling reveals that ESR1 mutations enhance cyclin-dependent kinase signaling

Three quarters of all breast cancers express the estrogen receptor (ER, ESR1 gene), which promotes tumor growth and constitutes a direct target for endocrine therapies. ESR1 mutations have been implicated in therapy resistance in metastatic breast cancer, in particular to aromatase inhibitors. ESR1 mutations promote constitutive ER activity and affect other signaling pathways, allowing cancer cells to proliferate by employing mechanisms within and without direct regulation by the ER. Although subjected to extensive genetic and transcriptomic analyses, understanding of protein alterations remains poorly investigated. Towards this, we employed an integrated mass spectrometry based proteomic approach to profile the protein and phosphoprotein differences in breast cancer cell lines expressing the frequent Y537N and Y537S ER mutations. Global proteome analysis revealed enrichment of mitotic and immune signaling pathways in ER mutant cells, while phosphoprotein analysis evidenced enriched activity of proliferation associated kinases, in particular CDKs and mTOR. Integration of protein expression and phosphorylation data revealed pathway-dependent discrepancies (motility vs proliferation) that were observed at varying degrees across mutant and wt ER cells. Additionally, protein expression and phosphorylation patterns, while under different regulation, still recapitulated the estrogen-independent phenotype of ER mutant cells. Our study is the first proteome-centric characterization of ESR1 mutant models, out of which we confirm estrogen independence of ER mutants and reveal the enrichment of immune signaling pathways at the proteomic level.

These studies have shown that ESR1 mutants often upregulate proliferation-related genes (e.g.cyclins), immunomodulatory signaling factors (e.g.Type I interferon), and display altered patterns of chromatin accessibility that affect transcriptional programs of other transcription factors other than the ER (e.g.OCT1 and CTCF) 10,15 .Despite this, profiling of protein abundance and phosphorylation levels have not been explored in ESR1 mutant breast cancers.
Mass spectrometry (MS)-based proteomic technologies have allowed in-depth characterization of cancer and other diseases 17,18 , and through integrated approaches such as proteogenomics, enable improved definition of tumor subgroups and the elucidation of altered pathways and driver mutations that could define cell fate and patient outcome 19,20 .Moreover, protein-centered analyses allow direct interrogation of the effectors of biological systems, facilitating the identification of biomarkers or drug targets 21,22 .On top of this, the analysis of phosphorylated proteins (phosphoproteomics) is capable of defining protein activation dynamics that modify the functional properties of cells (e.g.phosphorylation patterns during mitotic states) 23,24 .With these points in mind, we employed a combined proteomic and phosphoproteomic approach to define protein and phosphorylation patterns in ESR1 mutant models under estrogen deprivation (mimicking AI treatment) and estrogen stimulation conditions.By linking gene/protein expression changes to the activity of specific kinase signaling cascades might identify alternative therapeutic targets.We used MCF7 cells in which CRISPR-Cas9 methodology was used to introduce the Y537S 9 and the Y537N 25 mutations into the endogenous ESR1 gene (Fig. 1).In addition to this, we explored the proteome dynamics in a recently described T47D model bearing the Y537S mutation 10,15 .We show that the estrogen independent phenotypes of these models have different repercussions for proteome and phosphoproteome dynamics, depending on the cell model and type of mutation, though still promoting proliferation through inter-connected pathways.

ESR1 mutation analysis and external dataset comparison
In this study, we employed breast cancer cells expressing commonly observed ESR1 mutations (Y537N and Y537S) and wt ER counterparts and evaluated their growth in the presence/absence of estrogen (E2; Fig. 1).MCF7 Y537N (ref 25 ) and MCF7 Y537S (ref 9 ) cells, generated using CRISPR-Cas9 approaches, were estrogen-independent for growth (Fig. 2A).Genomic analysis and RT-qPCR have demonstrated that the MCF7 Y537N and MCF7 Y537S clones are heterozygous for their mutation 9,25 .MS analysis of cell lysates after rapid immunoprecipitation of endogenous proteins (RIME) using an ER antibody confirmed the presence of the relevant mutant and wt ER peptides in the MCF7 Y537N and MCF7 Y537S clones (Fig. 2B).Mutant peptides were absent in MCF7-wt cells.Of note, we did not detect any new ER interacting partner in ESR1 mutant over wt cells.
Next, we set to define estrogen dependent and independent changes by global proteome and phosphoproteome analysis.For this, we cultured MCF7 models in estrogen-free medium for 72 h (Fig. 1) prior to the addition of E2 or vehicle (ethanol, EtOH).Treatment duration was set to 6 h, consistent with the maximal activity/ stability of the ER transcriptional complex 22 .This analysis generated a list of 6468 proteins (Table S1) and 11,746 phosphopeptides (Table S2), respectively.
Given that potential discordance between proteomic datasets can arise from use of different cell batches, non-identical culture conditions, sample preparation (e.g. protein labelling vs label-free) and MS methodologies (e.g.instrument type, gradient length), we compared our proteomic data with those reported previously.The proteome profiles obtained for our MCF7-wt cells correlated well with the previously reported MCF7 proteome (Fig. 2C) 22 .The agreement between our protein expression profiles and those reported previously provided confidence for taking forward our datasets for further analysis.
As an additional dataset, we analyzed our MCF7 Y537S models in parallel with previously described T47D Y537S mutant 10,15 .Here, cells were stimulated with E2 or vehicle (dimethyl sulfoxide, DMSO) for 6 h and 24 h so to better capture early and late estrogen effects.As a baseline, proteomic data of these cells grown in full medium was also acquired.This second dataset is reported in Tables S3-4.

Protein abundance and phosphorylation differences reflect the ESR1 mutational status and estrogen treatment
We determined whether estrogen treatment and/or mutational status alter global protein and protein phosphorylation levels.PCA analysis of proteome data showed clear segregation of MCF7 wt cells dependent on estrogen treatment, while a weaker effect of estrogen was observed for MCF7 Y537N cells (Fig. 3A).By contrast, the PCA analysis indicated considerable change in the MCF7 Y537S proteome when compared with MCF7-wt, with little or no difference based on estrogen stimulation.PCA analysis of the phosphoproteome more clearly distinguished MCF7-wt from its mutants.Here MCF7-wt displayed differential clustering based on E2 treatment.MCF Y537N/Y537S co-clustered away from their wt counterpart, indicating smaller changes in phosphorylation that are dependent on estrogen, but also a significant shift in phosphorylation patterns when compared to wt cells, suggesting alterations in different kinase signaling cascades independent of estrogen (Fig. 3B).These groupings were confirmed by hierarchical clustering analyses (Fig. 3C,D).
When assessing these differences in our second dataset, we observed distinct clustering between MCF7 and T47D cells, with no co-clustering based on ESR1 genotype (Fig. S1A).PCA analysis of treated cells showed little distance in the PCA plane between cells harvested at 6 h, though vehicle and estrogen treated cells still formed sub-clusters.At 24 h cluster separation was more evident for both wt and mutant cells and was consistent for both MCF7 and T47D models (Fig. S1B-C).Phosphoproteome data of this second set displayed the same clustering, with clear separation between T47D and MCF7 models in the full medium subset, as well as separation between E2 and vehicle treatments across the two time points (Fig. S1D-F).
www.nature.com/scientificreports/Collectively, our data suggest that proteome and phosphoproteome alterations depend not only on the expression of wt or mutated forms of the ER and its activation by the presence of estrogens, but also on cell model.On this, metascape analysis 26 of ANOVA significant proteins and phosphosites (p < 0.05) between MCF7 and T47D cells (full medium subset) showed enrichment in proteins involved in transcription and RNA regulation both at the proteome and phosphoproteome levels (Fig. S2A-B).In addition to this, while differences between estrogen and vehicle stimulated cells are observed at the 6 h time point, these become more evident at 24 h, where late estrogen effects likely take place.
In order to further confirm the different response to estrogens across cell models we performed differential expression analysis on our proteome and phosphoproteome data, which showed that all cell models displayed changes in protein expression (t-test adjusted p-value < 0.05) between E2 and vehicle conditions (Table S5), with the exception of MCF7 Y537S , in line with PCA results (Fig. 4A).In the phosphoproteome dataset, differentially expressed phosphosites generally encompassed gains and losses (i.e.fully observed in one condition and completely absent in the other; Fig. 4B and Table S6), such as ARID3A phosphorylation at Ser77 observed in MCF7 Y537S (p-value < 0.01).These differences might be related to the different type of response to estrogen, and the consequent alteration of transcriptional programs, enacted across different cell models.On top of this, cells Experimental and data analysis workflow.We employed cell models expressing ESR1 mutations (MCF7 Y537N , MCF7 Y537S ) and their isogenic wt ER counterparts.Cells were cultured in estrogen-free media for 72 h, then treated with estradiol (E2) or vehicle (EtOH) for 6 h before harvesting.Cells were lysed, protein extracts digested, fractionated, and analyzed by MS.Proteome and phosphoproteome data was searched with MaxQuant, resulting output was filtered (decoy, false identification, missing data), and employed for downstream comparative analyses.Datasets were subsequently integrated to define, based on overlapping enrichment, alternative drug targets for validation., which can then explain these dissimilarities.
Next, we performed Gene Set and Kinase-Substrate Enrichment Analysis (GSEA 27 , KSEA 28 ) on our proteome and phosphoproteome data, respectively (Tables S7-8).Here, cell proliferation (e.g.mTORC1 signaling, MYC targets) and metabolism (e.g.oxidative phosphorylation) were enriched in mutant models in estrogen deprivation conditions, while in wt cells these gene sets were upregulated after E2 stimulation.Analysis for Reactome terms displayed similar results, with numerous pathways significantly enriched after estrogen in wt cells (Fig. S3A-B).Enrichment of cell cycle, transcription and signaling pathways were observed in wt cells under estrogen stimulation, while ESR1 mutants showed the same enrichments under estrogen deprivation (Fig. S3C, Table S9).In this set, KSEA results revealed activation of CDKs (i.e.CDK1, CDK2, CDK4) after estrogen stimulation in MCF7 models, with the exception of MCF7 Y537S , which displayed CDK upregulation under estrogen deprivation (Fig. 4C).
GSEA of our second dataset confirmed enrichment of ER signaling and proliferation pathways in both MCF7 and T47D wt models, with a more pronounced response in T47D cells.On top of this, while MCF7 Y537S treated with vehicle showed enrichment of pathways typically enriched in the wt counterpart when treated with estrogens, T47D cells showed a major response to estrogen stimulation, which stimulated immune (e.g.allograft rejection), proliferation (e.g.mTORC1 signaling), and metabolism (e.g.glycolysis) pathways (Fig. S4A).KSEA analysis of this additional set showed CDK activation after estrogens in MCF7-wt (CDK1/2/7), while the Y537S mutant showed positive but non-significant enrichment (Fig. S4B).
These results confirm that ESR1 mutant cells operate through similar molecular pathways (e.g.proliferation) as their wt counterparts, especially after estrogen withdrawal.

Cell proliferation and immune signaling pathways are enhanced in ESR1 mutant cells
Having established that cell models expressing ESR1 mutations display an altered response to E2, we investigated specific differences between each mutant and its wt ER isogenic counterpart (Fig. 5A,B and Tables S10-11).Here, commonly dysregulated proteins under estrogen deprivation across mutant cells mapped to pathways related to protein stabilization and folding (e.g.CCT7) as well as telomerase activity related networks (e.g.TCP1; source: DAVID v2021q1 29 ; Fig. 5A).Additionally, several ESR1 targets (e.g.FREM2, CKS1B) were enriched in ER mutants, especially at the protein level under estrogen deprivation, suggesting ligand-independent activation of the ER pathway in these models.These results show that cells expressing ER mutants not only display dysregulation of ESR1 signaling, but also show changes in other cellular pathways 5,6 .Closer investigation of genes responsive to ER (MSigDB v2021.1 ; ref 30 ) confirmed not only a different regulation for each mutant over wt cells, but also showed differences between protein and phosphosite abundances (Fig. S5), with little change at the protein level but marked differences within the phosphoproteome.
GSEA analyses performed on proteome data pointed out that differentially expressed proteins in ESR1 mutants related to cell cycle, proliferation, and immune signaling pathways (Fig. 5C).Similar results were observed when interrogating the Reactome database, where mutants showed pathway enrichment over wt cells mostly under  www.nature.com/scientificreports/estrogen deprivation (Fig. S6 and Table S12).Here, cell cycle and TGFβ pathways were found significantly enriched in mutants in absence of estrogens (Fig. S6B), while E2 stimulation highly enriched these networks in wt cells (Fig. S6C).Enrichment of immune signaling pathways, such as the Interferon-related signaling network, were described in previous studies and linked to immunomodulation of the cancer microenvironment 10 .Closer investigation of the reported gene sets revealed immune signaling protein enrichment in ER mutants (e.g.IFITM1; Fig. S7A-B).These gene sets displayed a large overlap with our GSEA results, especially with the INTERFERON GAMMA RESPONSE gene set, which displayed a significant enrichment in the MCF7 Y537S model (Fig. S7C).
Our second dataset showed similar results, with proliferation promoting pathways (e.g.MYC targets, E2F signaling) enriched in ESR1 mutants at 6 h under estrogen deprivation.Additionally, a higher number of enriched pathways was found in T47D models when compared to MCF7 cells.Despite of this, both models showed similar enrichment trends, with pathways enriched in mutant cells under estrogen deprivation found significant in wt cells under estrogens (e.g.MYC targets, interferon gamma response, PI3K/Akt/mTOR signaling; Fig. S8A).Further investigation of ESR1 mutant in a clinical breast cancer cohort (SCAN-B 31 ; n cases = 27; n controls = 108, see Methods for details) largely confirmed our cell model results (top enriched pathways: E2F targets, G2M checkpoint, MYC targets, and mTORC1 signaling; Fig. S8B).www.nature.com/scientificreports/KSEA analyses on the phosphoproteome dataset comparing ER mutants and their wt isogenic cells revealed multiple kinase activations, with CDK1/2/4/5/9, and mTOR enriched in MCF7 mutant models under estrogen deprivation.Moderate enrichments of CDKs and other kinases (e.g.MAPK8) over wt cells were observed under estrogen stimulation (Fig. 5D).Closer inspection of CDK network expression showed a higher abundance of CDK4 and CDK6 in MCF7 Y537S at the protein level and regardless of treatment (Fig. S9A), while phosphorylation of CDKs was evident in this model under estrogen deprivation (Fig. S9B).CDK target enrichment was especially pronounced in mutant cells under estrogen deprivation (Fig. S9C), with abundance enrichments at the phosphosite level rather than the protein one (Fig. S9D-E).We assumed these enrichments might relate to Cyclin protein expression, which showed increased expression in MCF7 Y537S at the protein level (e.g.CCNY, CCND1; Fig. S9F), but only wt cells showed increased phosphorylations (under estrogens; Fig. S9G).Our additional dataset confirmed CDK enrichment in ESR1 mutants over wt cells, both in full medium and after estrogen or vehicle treatment (e.g.CDK2, CDK4; Fig. S10).www.nature.com/scientificreports/Altogether these data confirm that ER mutants appear to enact their phenotype through the upregulation of proliferation and mitotic pathways, which in turn are responsible for growth in estrogen deprivation conditions.Despite these results, inhibition of key pro-mitotic pathways (i.e.mTOR and CDK inhibition) did not show significant differences in growth inhibition effects comparing ESR1 mutant cells to wt MCF7 (Fig. S11), leading us to believe other factors might be involved in sustaining proliferative signaling and promoting resistance to mTOR and CDK inhibitors [32][33][34] .To obtain an additional angle to define these putative factors, we wondered whether integration of the proteomic data layers might provide additional information for target selection.

Integrated analysis shows model-dependent proteome-phosphoproteome regulation of splicing machinery
Having assessed that the proteome and phosphoproteome undergo different levels of regulation between ESR1 mutant cells but converge at the pathway level, we investigated our merged proteome and phosphoproteome data.The resulting dataset was particularly enriched in RNA processing proteins (Fig. S12A).Spearman correlations over the whole dataset revealed that protein and phosphosite abundance were either de-coupled (no correlation) or positively correlated (Fig. S12B and Table S13).Enrichment analysis for Gene Ontology Biological Process (GOBP) terms showed that RNApol-II transcription pathways displayed positive correlations, while cell cycle and splicing attributes showed decoupled or negative correlation (Fig. S12C).Distributions of Spearman coefficients displayed different trends in each model, where MCF7-wt and MCF7 Y537N models showed negatively oriented correlation distributions, while in the MCF7 Y537S mutant the distribution was centered on 0 (Fig. S12D).Here, GOBP enrichment analyses displayed similar results to the one performed on the whole dataset (Fig. S12E).Agreement between proteome and phosphoproteome appeared to be model-dependent, with no discernible impact based on treatment.
This data shows that while significant correlations between proteome and phosphoproteome changes are mostly positive, cells expressing ESR1 mutations often display shifts in protein-phosphorylation regulation when compared to their wt counterparts, supporting the idea that kinase signaling plays an important role in regulating protein abundance.

Proteomic and phosphoproteomic changes pinpoint targets for alternative therapies
Having established that proteome and phosphoproteome of ESR1 mutant cells display a degree of co-regulation when compared to wt models, we wondered whether the overlap between protein and phosphosite enrichments might pinpoint alternative targets for therapy.Here, we observed that subsets of proteins were enriched at both the proteome and phosphoproteome levels in our ER mutant (e.g.NCOR1), with dysregulation of both ER responsive genes and interactors (e.g.RBBP7, ADNP; Fig. 6A,B).We argued that selection of alternative therapeutic targets should include not only our datasets, but also functional assays.With this in mind, we cross-referenced our proteome and phosphoproteome datasets against CRISPR screen-derived ER mutant essential genes 11 for which FDA approved drugs are available (source: www.prote inatl as.org/ human prote ome/ drugg able) 35 .In this subset, protein and phosphosite data across wt and mutant ER models showed enrichment of proteins involved in gene expression regulation (e.g.HDAC1/7), metabolism (e.g.CAT), and cytoskeletal (e.g.MAP4) proteins.Of note, proteome data pinpointed PSMB10 as constitutively enriched in ESR1 mutants, regardless of treatment (Fig. 6C), while protein phosphorylation showed upregulation of this protein especially in ER mutant MCF7 cells (Fig. 6D).PSMB8, PSMB9 and PSMB10 are part of the immunoproteasome complex, which is activated in response to interferon gamma signaling, and has been previously associated to cancer cell survival and progression [36][37][38][39] .Having observed enrichment of the immunoproteasome complex and immune signaling pathways in our proteomic data, as well as transcript upregulation in ER mutant MCF7 models out of RNA-sequencing (RNA-Seq) data (Fig. 6E) 9 , we determined whether inhibition of the immunoproteasome could constitute an alternative therapeutic strategy.We employed commercial inhibitors of the proteasome, which act by blocking (reversibly or irreversibly) proteasome activity, promoting the accumulation of ubiquitinated proteins and inducing cell cycle arrest and apoptosis.The observed effect on cell growth for the three commercial inhibitors (i.e.Bortezomib, Carfilzomib, Ixazomib) did not show any difference between cells expressing wt and mutated ESR1 as single agent treatment (Fig. S13A-C).When testing Bortezomib combination treatment with Fulvestrant, as also tested in phase II clinical trials 40 , we observed an additive effect between the two drugs, though inhibition effects were much more pronounced in wt cells when compared to the MCF7 Y537S model (Fig. 7).This might be due to the lack of specificity of these inhibitors in targeting the immunoproteasome (i.e.PSMB8/9/10).Alternatively, the impact of these upregulated proteins in elevated interferon signaling would facilitate metastatic processes by remodeling tumor-associated immune responses in vivo, as has been proposed 10 rather than being critical for enhanced proliferation.On top of this, other in vitro factors dependent on 2D culture (reviewed in 41 ) might be responsible for the limited response to the tested drugs.

Discussion
About three quarters of breast cancer cases express the ER, which drives their growth and proliferation, hence impacting patient prognosis.While treatment of primary tumors remains effective through surgery and adjuvant endocrine therapies (in particular AIs), recurrent breast cancers remain the most frequent cause of death 1,42 .Recurrent tumors are often refractory to therapies due to acquired mutations, cross-talk mechanisms, etc. 2 , and new therapeutic strategies are needed.Mutations in the ligand binding domain of the ER have been shown to confer resistance to AI therapy by promoting ligand-independent activation of the receptor, but also by altering cellular signaling beyond the ER pathway 5,8 .So far, molecular changes enacted by ESR1 mutations have been largely evaluated through epigenetic and genomic technologies 9,14 .Here we employed an integrated proteomic approach to characterize protein and phosphorylation networks in ESR1 mutant cells across estrogen stimulation and deprivation conditions to extrapolate key mechanisms responsible for therapy resistance.
In this study, we employed cancer cell models engineered to harbor ESR1 mutations (MCF7 Y537N/Y537S ), as well as an external set of mutated T47D (Y537S mutation).Each model was analyzed in relation to protein and phosphosite changes in response to estrogen deprivation and stimulation conditions, and against its isogenic counterpart expressing wt ER.
Our global analysis of the proteome and phosphoproteome datasets showed that mutant cells clustered separately not only from their wt counterparts.Here, MCF7 and T47D cells displayed variable responses to estrogen deprivation and stimulation in relation to differential protein expression, phosphorylation patterns, and pathway activation.Despite of this, common activation of oncogenic pathways (e.g.MYC response genes) was observed across our mutant models, complementing previous observations 10,15 .For this, while cell line-specific responses were not surprising, the integrative analysis of different cell models provides an outlook into cancer diversity and enables to discriminate proteins and pathways common to ESR1 mutants.
When assessing response to estrogens, we noted that only MCF7 Y537S did not show any changes in terms of differentially expressed proteins, indicating this mutant as unresponsive to estrogen signaling.Conversely, the MCF7 Y537N mutant displayed estrogen response similar to its wt counterpart (Figs. 3 and 4), which could reflect the observed higher sensitivity to SERDs when compared to the MCF7 Y537S mutant 5,25 .Despite this, a marked enrichment of proliferation (e.g.MYC targets) and metabolism (e.g.oxidative phosphorylation) pathways was observed across MCF7 mutants.In addition, phosphoproteomic analysis confirmed the activation of CDKs across mutant models and in wt cells under estrogen stimulation, directly linking the growth phenotype of these cells and molecular changes at the protein level.Our second dataset comprising T47D Y537S showed a higher response to estrogen stimulation, where we found enrichment of immune signaling and metabolism on top of proliferation pathways (Fig. S4A).The relationship between ESR1 mutant expression and CDK activity has been pinpointed in previous studies, especially in relation to CDK7-dependent phosphorylation of Ser118, which has activating effects on the ER 9 .While ER phosphosites were not detected in our dataset, we nonetheless demonstrate a significant contribution of CDKs towards the establishment of the estrogen independent phenotype in ESR1 mutant breast cancer cells.
Upon comparing mutant to wt cells, we observed distinct protein abundance changes, even among ER targets (e.g.CRTC1).Here, we observed moderate upregulation of ER targets in ESR1 mutants over wt cells at the proteome level, but more evident changes were detected for phosphosites.On top of this, proliferation (e.g.E2F targets) and immune signaling (e.g.interferon) pathways were enriched in mutant cells, with kinase activity analyses pointing out a major role of CDKs and mTOR in driving the increased phosphorylation patterns.These findings were confirmed not only in our analysis of the T47D Y537S model but also in a set of primary breast tumors, where proliferation pathways were highly enriched in mutant tumor cells (Fig. S8).Growth factor-related Tyr kinases and CDK activity have been previously linked to breast cancer endocrine resistance through altered phosphorylation patterns and modulation of ER signaling (reviewed in 7,43 ).Recent studies observed increased activity of these pathways in ESR1 mutants out of transcriptome analyses 9,14 .CDKs regulate cell cycle progression and activate (through phosphorylation) a series of proteins involved in RNA processing (e.g.splicing) and transcription (e.g.POLR2A).Inhibition of CDK4 and 6 currently constitutes the standard of care for recurrent ER www.nature.com/scientificreports/positive breast cancers in combination with endocrine therapies, though progression under such treatment may develop through (e.g.) activation of Tyr kinase receptors 44 .No difference in sensitivity to CDK4/6 inhibition was observed between wt and ESR1 mutant models, suggesting several proliferation axes operating in these models.While activation of these upstream kinases may vary, immune pathways in ESR1 mutated tumors might have a prominent role in activating CDK signaling through cross-talk mechanisms 7,45 .Inflammation and immune signaling networks (e.g.Interferon-related pathways) have been linked to immunosuppressive macrophage infiltration in breast cancers with mutated ERs 10 .While immune signaling pathways may provide a selective advantage within the tumor microenvironment towards immune escape (e.g.Interferon gamma response), their activation may also be responsible for cell growth and proliferation via non-canonical activation of the mTOR pathway 45 , which enrichment has been repeatedly reported in ESR1 mutants together with metabolic reprogramming and enhance cell growth and proliferation 9,14,33,34 .
Building on the idea that proliferation-associated pathways were found enriched at the protein abundance and phosphorylation levels, we overlaid the proteome and phosphoproteome datasets to define proteins and mechanisms responsible for the estrogen independent phenotype.While proteome and phosphoproteome varied greatly depending on protein function (e.g.RNA transcription vs GTPase activity), common enrichments were observed (e.g.STT3B protein levels and S498 phosphorylation).Protein abundance and phosphorylation levels are respectively dependent on (e.g.)rate of transcription and kinase activity, which in turn explain the discrepancies observed.In addition to this, the low correlation between protein abundance and phosphorylation levels of proteins involved in gene regulation might relate to the significant enrichment of proliferation pathways observed in our differential expression and pathway analyses (Figs. 4 and 5).With this in mind, we crossreferenced our proteomic datasets with previously performed CRISPR-screens 11 and RNA-seq data 9 to select targets for further verification.Here we observed enrichment in components of the immunoproteasome (PSMB10) in ESR1 mutants, the expression of which is related to activation of the interferon gamma signaling pathway.Given the lack of commercial immunoproteasome inhibitors, we employed 3 inhibitors of the 20S/26S proteasome [46][47][48] , although no difference in growth was observed between wt MCF7 and ESR1 mutants.A recent report demonstrated profound differences in the makeup of tumor associated immune cells for metastatic tumors from ER mutants compared with those from wt ER, including higher levels of regulatory and helper T cells and immune-suppressive macrophages 10 , raising the possibility that the elevated interferon signaling proteins identified here have an immunosuppressive role in aiding the metastatic potential of ER mutant breast cancer.On top of this, proteasome inhibition coupled with Fulvestrant treatment was tested in a phase II clinical trial, with enhanced effects in AI resistant breast cancers 40 .While we could not show that inhibition of the immunoproteasome, as part of interferon gamma signaling, had any effect on ESR1 mutant cell growth rates over wt cells recent studies have revealed the activation of immune pathways through the acquisition of basal tumor features, indicating a role towards immune escape and tissue invasion rather than just proliferation 49,50 .Further investigation of these mechanisms might offer new avenues for immunotherapy regimens through in vitro co-culture experiments (e.g.tumor cells and leukocytes) and in clinical specimens (tumor and blood).This experimental venue would be able to clarify the interaction between the immune system, ESR1 mutated breast cancers, and the signaling between these two actors.
We present here the first proteomic-centered analysis of ESR1 mutant cells, where we characterized changes in total protein abundance and phosphorylation over estrogen deprivation and stimulation conditions.The data presented here offers a new angle on the elucidation of estrogen independent growth associated to the expression of ER mutations, constituting a resource for further investigation.

Cell lines and culturing
MCF7-wt, MCF7 Y537N and MCF7 Y537S , have been previously described 9,14,25 .The T47D (wt and Y537S) were described in Williams et al. 10 .All cell lines were cultured in Dulbecco's modified Eagle's medium (DMEM supplemented with GlutaMAX™; Thermo-Fisher) containing 10% fetal bovine serum (FBS; Gibco) supplemented with minimal essential aminoacids (MEAA; Thermo-Fisher) and antibiotics (100 units/mL penicillin G; 0.1 mg/ mL streptomycin; 45 µg/mL Gentamycin; Thermo-Fisher).For estrogen stimulation experiments, cells were cultured for 72 h in phenol red-free media supplemented with 10% charcoal-stripped FBS (csFBS; Gibco) and antibiotics (as above).Subsequently, medium was changed and E2 solubilized in ethanol was added to a final concentration of 1 nM.An equal volume of EtOH (vehicle) was added to the no ligand controls.Cells were incubated for 6 h before harvesting.When treating MCF7 and T47D cells included in the second dataset, di-methylsulfoxide (DMSO) was used as vehicle and final concentration of E2 was 10 nM.Here, harvesting was performed at 6 h and 24 h.In addition to this, full media harvests were conducted in parallel.All estrogen stimulation/deprivation experiments were conducted for 3 biological replicates.

Cell growth assays
For cell growth assays, cells cultured in estrogen-depleted medium for 72 h were seeded into 96-well plates (Thermo-Fisher) at a concentration of 5000 cells/well (3 wells per cell line/condition in each biological replicate).After attachment (~ 12-16 h), medium was changed and supplemented with either E2 (Sigma-Aldrich) or EtOH as vehicle.The Sulphorhodamine B (SRB) assay was then used to determine cell numbers 9 .Briefly, cells cultured in 96-well plates were fixed by adding 100 μL/well of 40% trichloroacetic acid (Sigma) and incubated at 4 °C for 1 h.Plates were washed six times with distilled water and air-dried.Total cellular protein was stained by adding 100 μL/well of 0.4% (w/v) Sulphorhodamine B-SRB (Sigma) in 1% acetic acid (VWR) and incubated at RT for 1 h.Plates were washed 6 times with 1% V/V acetic acid to remove the unbound dye and air-dried overnight.A www.nature.com/scientificreports/ was performed according to manufacturer instructions, where washing step eluates from the TiO 2 enrichment step were loaded on the Fe-NTA ones.Eluates from both kits were dried and stored at -80 °C until MS analysis.Peptides from MCF7 and T47D cells were labeled using TMT6plex (Thermo Scientific) and fractionation was carried on using high-pH spin columns (Thermo Scientific) according to manufacturer instructions.About 95% of each eluate was further processed for TiO 2 -based phosphopeptide enrichment, while the remaining 5% was employed for proteome analysis.Peptide and phosphopeptide fractions were desalted as above.

Mass spectrometry analysis
RIME peptide mixtures were analyzed on a Q-Exactive Plus, coupled to a nano LC Easy system (Thermo-Fisher).Peptides were separated on an EasySpray HPLC column (ID 75 µm × 25 cm C18 2 µm, 100 Å resin; Thermo-Fisher) in a 110 min gradient (flow: 300 nL/min; mobile phase A: 0.1% formic acid in H 2 O; mobile phase B: 100% acetonitrile and 0.1% formic acid).The gradient was run as follows: 5% B for 5 min; 5-30% B in 90 min; 30-95% B in 5 min; 95% B for 10 min.The 15 most abundant peaks from the MS scan (resolution: 70,000 at 200 m/z) were selected and fragmented by higher energy induced collision dissociation (HCD; collision energy: 30).Automatic Gain Control (AGC) target for full MS and MS/MS scans was set to 1E6.
As with the proteome runs, phosphoproteome peptide mixtures were analyzed on a Q-Exactive HF-X (Thermo-Fisher) system coupled to a nano-LC easy 1200 chromatography system (Thermo-Fisher).Trapping, analytical columns, mobile phases and flow employed in this analysis were the same as above.Gradient was as follows: 10-30% B in 90 min; 30-45%B in 20 min; 45-95% B in 1 min; 95%B for 9.5 min.Full MS scan parameters: 60,000 resolution, AGC target was set to 3E6.MS/MS scan parameters: 15,000 resolution, AGC target was set to 1E5.Collision energy was set to 28, with 15 analyzed peaks and a 10 s dynamic exclusion window.
TMT-pools were analyzed on a Q-Exactive HF-X (Thermo-Fisher) system coupled to a nano-LC easy 1200 chromatography system (Thermo-Fisher).Trapping, analytical columns, mobile phases and flow employed in this analysis were the same as above.Gradient was as follows: 5-25% B in 110 min; 25-40%B in 10 min; 40-95% B in 10 min; 95%B for 0.5 min.Full MS scan parameters: 120,000 resolution, AGC target was set to 3E6.MS/MS scan parameters: 45,000 resolution, AGC target was set to 1E5.Collision energy was set to 34, with 20 analyzed peaks and a 30 s dynamic exclusion window.

Mass spectrometry data processing
MS analysis-derived RAW files were analyzed using MaxQuant 56 (v1.6.14.0), and MS spectra searched using the Andromeda 57 search engine with the Uniprot-Swissprot human proteome database (version download: 2020.02.24; second dataset: 2022.04.02).For RIME analysis MaxQuant (v1.5.5.1) was employed and modified ESR1 peptide sequences were collated, generating a new FASTA file (from Uniprot version download: 2017.06.23).Selected protease was Trypsin.Carbamidomethylation of Cys residues was selected as fixed modification (set as variable modification for RIME data search), while Met oxidation and acetylation of N-terminal residues were selected as variable ones.For the phosphopeptide search, phosphorylation of Ser/Thr/Tyr residues was additionally selected as variable modification, while Label-free Quantification (LFQ) was activated for the proteome data search.Identification of peptides resulting from missed cleavages was allowed.Precursor ion tolerance: 20 and 4.5 ppm for first and main searches, respectively.Match-between-run option was enabled and settings left to default.External dataset proteome files 14,22 were searched using the same settings as our proteome dataset, but enabling dimethyl and Tandem Mass Tag settings, respectively.For analysis of TMT pools, MS2 reporter ion options were enabled and TMT6plex was selected.Filtering on minimum reported precursor intensity fraction was set to 0.75.Other parameters were set as above.
Protein and phosphorylated peptide intensity files (label-free and TMT) were employed for downstream proteome and phosphoproteome searches, respectively.The protein abundance table was filtered for protein q-value (< 0.01), contaminant (excluded), reverse sequences (excluded), unique peptides (at least 1).The phosphopeptide table was additionally filtered for localization probability (> 0.75) of phosphorylations.

Clinical tumor cohort
A set of 28 primary breast tumors with detected ESR1 mutation and 108 control cases were derived from the SCAN-B tumor cohort 31 .Control cases were selected based on ER status (positive; cutoff 10% of positive cells), PgR status (positive; cutoff 10% of positive cells), adjuvant endocrine treatment, no ERBB2 amplification, and Luminal (A or B) subtype.This resulted in a final set of 26 cases and 81 controls.Gene expression in Transcripts per Kilobase Million (TPM) were employed for pathway analysis using GSEA.

Figure 1 .
Figure 1.Experimental and data analysis workflow.We employed cell models expressing ESR1 mutations (MCF7 Y537N , MCF7 Y537S ) and their isogenic wt ER counterparts.Cells were cultured in estrogen-free media for 72 h, then treated with estradiol (E2) or vehicle (EtOH) for 6 h before harvesting.Cells were lysed, protein extracts digested, fractionated, and analyzed by MS.Proteome and phosphoproteome data was searched with MaxQuant, resulting output was filtered (decoy, false identification, missing data), and employed for downstream comparative analyses.Datasets were subsequently integrated to define, based on overlapping enrichment, alternative drug targets for validation.Acronyms: E2: 17-β-estradiol; ER: estrogen receptor; EtOH: ethanol; MS: mass spectrometry.

Figure 2 .
Figure2.Mutational analysis and comparison proteomic profiles in this study to public datasets.Panel A represents results of Sulforhodamine-B growth assays of MCF7 cells.Cells, pre-cultured in estrogen-free medium, were treated with estrogen (E2; 1 nM) or an equal volume of vehicle (veh; ethanol).Panel B displays MS-based detection of mutant (Y537N: NVVPLNDLLEMLDAHR; Y537S: NVVPLSDLLEMLDAHR) and wt ER (NVVPLYDLLEMLDAHR) peptides in MCF7 models, performed using MS analysis of ER immuneprecipitates (RIME MS).Panel D depicts scatter plots of overall protein abundance correlations between the proteomic data generated in this study and a previously published datasets and MCF7-wt (PXD007968).Acronyms: E2: 17-β-estradiol; ER: estrogen receptor; EtOH: ethanol; MS: mass spectrometry; RIME: rapid immunoprecipitation of endogenous proteins.

Figure 3 .
Figure 3. Principal Component analyses of proteome and phosphoproteome data.Figure displays global PCA analyses for proteome (panel A) and phosphoproteome (panel B) data for all cell models under estrogen stimulation (E2) and deprivation (EtOH) conditions.Panels C and D display hierarchical clustering heatmaps based on proteome and phosphoproteome data, respectively.Acronyms: E2: 17-β-estradiol; ER: estrogen receptor; EtOH: ethanol; MS: mass spectrometry.

Figure 4 .
Figure 4. Protein and phosphosites-centered analysis of response to estrogens.Figure displays proteome and phosphoproteome abundance shifts across wt and mutated ER cell models after treatment with EtOH and E2.Panels A and B display scatter plots of differentially expressed (t-test adjusted p-value < 0.05) proteins and phosphosites between E2 and EtOH conditions per each cell model.Dark blue contoured dots represent ESR1 target genes (source: ENCODE), while filled dots represent pathway annotation.Panel C show heatmap of GSEA results for MCF7 models.Panel D shows KSEA results for MCF7 models where phosphorylation patterns were compared between E2 and EtOH conditions.Positive KSEA Z-scores define enrichment under estrogen, while negative scores portray EtOH enrichment.Acronyms: E2: 17-β-estradiol; ER: estrogen receptor; EtOH: ethanol; MS: mass spectrometry; GSEA: gene set enrichment analysis; KSEA: kinase substrate enrichment analysis.

Figure 5 .
Figure 5.Protein and phosphosite enrichment analysis of ESR1 mutants.Protein and phosphosite enrichment analyses were performed on ESR1 mutants in comparison with isogenic wt ER cells across E2 and EtOH conditions.Panels A and B display scatter plots of differentially expressed (t-test adjusted p-value < 0.05; dark contour dots: ER targets; filled dots: pathway annotation) protein and phosphosites between mutant and wt cells after EtOH (left) and E2 (right).Panel C show heatmap of GSEA analyses MCF7 mutants.Panel E represents KSEA analyses results (mutant vs wt) after EtOH (left) and E2 (right) treatment.Positive KSEA Z-scores score define enrichment in ESR1 mutants, while negative scores portray enrichment in wt cells.Acronyms: E2: 17-β-estradiol; ER: estrogen receptor; EtOH: ethanol; MS: mass spectrometry; GSEA: gene set enrichment analysis; KSEA: kinase substrate enrichment analysis.

Figure 6 .
Figure 6.Integrated analysis of ER mutant cells.Figure displays the results of the combined analysis of mutant enrichments across all data layers.Panels A and B show enrichments of protein and phosphosites in ER mutants and wt ER isogenic cells (significance in both data layers is represented in full color; red dots: ER interactors; blue dots: ER targets).Panels C and D represent expression heatmaps of essential gene products targetable by FDA approved drugs at the proteome and phosphoproteome level, respectively.Panel E displays transcript enrichment of the immunoproteasome in ER mutants out of RNA-sequencing data.Acronyms: E2: 17-β-estradiol; ER: estrogen receptor; EtOH: ethanol; MS: mass spectrometry; GSEA: gene set enrichment analysis; KSEA: kinase substrate enrichment analysis.

Figure 7 .
Figure 7. Drug synergy assessment with endocrine therapy.Figure displays the response curves for proteasome inhibitor Bortezomib in combination with Fulvestrant (upper plots) for MCF7-wt (panel A) and the MCF7 Y537S (panel B) models.Synergy scores were then calculated based on the Bliss model (see Methods section for details) and displayed for every concentration in heatmaps (lower plots).